How AI Will Change Performance Testing Forever with Scott Moore

Scott Moore, who has witnessed the evolution of performance testing since the early days of Mercury Interactive, highlighted how AI is addressing long-standing pain points in the industry. One of the most significant challenges has always been the time-consuming nature of correlation in performance testing.

“The correlation in performance testing has been a freaking nightmare," Alex Zap pointed out, referencing the tedious process that performance engineers have struggled with for decades. Today's AI-powered tools can now automate this process, with correlation managers that build correlation rules automatically, drastically reducing the effort required.

A central theme throughout the conversation was the importance of integrating performance testing earlier in the development lifecycle.

“If something doesn't work well for one user, it's not going to work well for many users," Scott emphasized, echoing a sentiment often shared by performance testing experts. “Why can't you get timing at the functional testing point? If the timing is bad when you push that button, it's not going to get any better when you throw more load on the server."

This philosophy is driving the development of tools that empower non-performance specialists to contribute to performance testing efforts. As AI-driven tools become more sophisticated, they're enabling functional testers to identify performance issues before they're handed off to specialized performance engineers.

Perhaps the most powerful insight from Scott was framing performance engineering as an expression of customer care.

“Does it matter if our customers are happy? Does it matter if our software is efficient? Does it matter if the software costs less?" Scott asked rhetorically. “When you apply performance engineering to a product and make it better, it proves that you love your customers."

This sentiment resonated throughout the discussion as the panel explored how AI is making it easier for companies to demonstrate this customer-centric approach.

Looking ahead, Scott and Alex shared their perspectives on how AI will transform performance testing:

1. Automated Correlation and Analysis : AI will continue to improve automated correlation, making it accessible even to those without deep technical expertise.

2. Intelligent Test Generation : AI can analyze business requirements and generate appropriate performance test scenarios without extensive manual planning.

3. Unified Testing Approaches : The lines between functional and performance testing will blur as AI enables the repurposing of scripts across different testing types.

4. Faster Time to Results : “Get me the data that I need to make a decision," Scott emphasized. AI is dramatically shortening the time between test execution and actionable insights.

Both Scott and Alex acknowledged that enterprise adoption of AI-driven performance testing has been cautious. However, as Scott pointed out, “AI in 2025 is not AI from 2023. It's just so fast that it is changing."

Alex added that competitive pressures and board-level mandates to improve productivity through AI will accelerate adoption: “2026 will be taking in budgeting with cutting the cost adjustment to certain percentage, say 25-30 percent of the productivity that board of directors will expect for CIOs to drive from the AI-infused processes."

The conversation made it clear that AI is not just enhancing performance testing—it's fundamentally transforming it. By automating tedious tasks, enabling earlier detection of issues, and making performance testing more accessible to the broader QA team, AI is helping organizations deliver better customer experiences.

As Scott succinctly put it: “If you don't love your customers, why should they expect to use your software?" In the AI era, performance testing is becoming both easier to implement and more critical than ever.